Most municipal and private-sector infrastructure leaders seldom if ever think about how technologies like artificial intelligence (AI) and machine learning can help improve physical systems like roads, mass transit networks or water utilities.

Although many cities are starting to recognize the importance of data, only a handful are prioritizing data collection and even fewer are feeding that data to advanced algorithms that can improve decision-making. This is understandable since the public and private sector leaders responsible for our infrastructure typically have little time to think about the future while working tirelessly to ensure that people can get to work in the morning and that the water flows when people turn on the faucet.

Yet, the opportunities presented to infrastructure decision-makers by the advent of data analytics, AI and machine learning are fast becoming too compelling to ignore. From predicting where repairs or new infrastructure will be needed to automating mass transit to improving project management and contractor coordination to optimizing the movement of everything from cars to commodities, digital intelligence can help cut costs, increase efficiency and enhance outcomes.

For instance, Kansas City, Missouri, in partnership with Xaqt, is using machine learning algorithms to crunch information from in-road sensors, video cameras, weather data and other sources to predict and preempt pothole development. Many cities rely on reports from drivers or have to manually review video camera imagery to identify potholes — a reactive, rather than proactive, approach that creates a backlog of repair work. Kansas City, however, is now able to predict potholes with an 85% accuracy rate, resulting in a 30% cost savings.

Pittsburgh, working with Rapid Flow Technologies, is installing artificially intelligent traffic lights that use machine vision and sensor data to adapt to road conditions in real-time rather than at set intervals. The system uses predictive algorithms to manage traffic lights dynamically and sends that data to adjacent intersections to optimize the entire signal network. This stands in stark contrast to most cities that are forced to manually program light times — a task that often necessitates time-consuming research. Pittsburgh’s smart lights on the other hand are estimated to have reduced wait times by over 40%, translating into a 25% reduction in travel time.

Beyond roads, the adoption of artificial intelligence in more privatized infrastructure-related sectors such as shipping and energy has been even quicker. Los Angeles’ TraPac terminal has invested heavily in AI and robotics over the last few years, making the terminal one of the nation’s most automated seaports. Shipping giant DHL has developed an AI-based application to predict freight delays a week in advance. In energy, electrical utilities like Florida Power & Light and Duke Energy are using machine learning and pattern recognition algorithms to detect potential plant issues before they cause service disruptions. Many of these same utilities use machine learning to increase windmill efficiency and reduce maintenance costs.

The TraPac LLC shipping terminal, seen over the Port of Los Angeles in Los Angeles, California, U.S., on Wednesday, March 9, 2016. (Tim Rue/Bloomberg)

In other sectors, like water, AI has been slower to catch on but is starting to make waves. Imagine H20, a nonprofit water technology startup accelerator in San Francisco, chose two AI startups out of 12 total companies for its 2018 accelerator cohort. One of those startups, Pluto AI, uses artificial intelligence to predict plant conditions and recommend optimal operating parameters for water treatment plants. The company is already testing its solution at plants around the country and has successfully helped the Hallsdale-Powell utility district in Tennessee manage overflows at one of its wastewater treatment plants.

The opportunities for improved efficiency and effectiveness presented by artificial intelligence and machine learning are simultaneously immense and daunting.

As the infrastructure world becomes saturated with progressively sophisticated digital technologies, public and private sector infrastructure leaders will be forced to adopt a new base of knowledge and new set of skills. Many of these decision makers are accomplished engineers, but with mechanical, civil, structural or electrical backgrounds. Their expertise and experience remains valuable and relevant, but must today be augmented by perspectives from computer science and software engineering in order to meet the demands and expectations of today’s citizen-consumers. These changes also mean officials will have to source new partners and vendors — ones like Xaqt, Rapid Flow Technologies and Pluto AI that can supplement traditional infrastructural intelligence with digital intelligence.

As a growing number of municipalities set their sights on becoming “smart cities,” it’s important to remember that automation, sensors and data are only the beginning. Once you’ve established more automated systems and collected more granular data, you have to figure out what to do with it. Artificial intelligence and machine learning can help shoulder that burden by managing those systems and making use of that data, marrying physical and digital technologies in ways that help maintain the infrastructure we have while improving it in ways we never thought possible.